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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionalicloud-ai-video-wan-videoExecute the skills CLI command in your project's root directory to begin installation:
Fetches alicloud-ai-video-wan-video from cinience/alicloud-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate alicloud-ai-video-wan-video. Access via /alicloud-ai-video-wan-video in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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Category: provider
mkdir -p output/alicloud-ai-video-wan-video
python -m py_compile skills/ai/video/alicloud-ai-video-wan-video/scripts/generate_video.py && echo "py_compile_ok" > output/alicloud-ai-video-wan-video/validate.txt
Pass criteria: command exits 0 and output/alicloud-ai-video-wan-video/validate.txt is generated.
output/alicloud-ai-video-wan-video/.Provide consistent video generation behavior for the video-agent pipeline by standardizing video.generate inputs/outputs and using DashScope SDK (Python) with the exact model name.
Use one of these exact model strings:
wan2.6-t2vwan2.6-t2v-uswan2.2-t2v-pluswan2.2-t2v-flashwan2.6-i2v-flashwan2.6-i2vwan2.6-i2v-uswanx2.1-t2v-turbopython3 -m venv .venv
. .venv/bin/activate
python -m pip install dashscope
DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials (env takes precedence).prompt (string, required)negative_prompt (string, optional)duration (number, required) secondsfps (number, required)size (string, required) e.g. 1280*720seed (int, optional)reference_image (string | bytes, optional for t2v, required for i2v family models)motion_strength (number, optional)video_url (string)duration (number)fps (number)seed (int)Video generation is usually asynchronous. Expect a task ID and poll until completion.
Note: Wan i2v models require an input image; pure t2v models such as wan2.6-t2v can omit reference_image.
import os
from dashscope import VideoSynthesis
# Prefer env var for auth: export DASHSCOPE_API_KEY=...
# Or use ~/.alibabacloud/credentials with dashscope_api_key under [default].
def generate_video(req: dict) -> dict:
payload = {
"model": req.get("model", "wan2.6-i2v-flash"),
"prompt": req["prompt"],
"negative_prompt": req.get("negative_prompt"),
"duration": req.get("duration", 4),
"fps": req.get("fps", 24),
"size": req.get("size", "1280*720"),
"seed": req.get("seed"),
"motion_strength": req.get("motion_strength"),
"api_key": os.getenv("DASHSCOPE_API_KEY"),
}
if req.get("reference_image"):
# DashScope expects img_url for i2v models; local files are auto-uploaded.
payload["img_url"] = req["reference_image"]
response = VideoSynthesis.call(**payload)
# Some SDK versions require polling for the final result.
# If a task_id is returned, poll until status is SUCCEEDED.
result = response.output.get("results", [None])[0]
return {
"video_url": None if not result else result.get("url"),
"duration": response.output.get("duration"),
"fps": response.output.get("fps"),
"seed": response.output.get("seed"),
}
import os
from dashscope import VideoSynthesis
task = VideoSynthesis.async_call(
model=req.get("model", "wan2.6-i2v-flash"),
prompt=req["prompt"],
img_url=req["reference_image"],
duration=req.get("duration", 4),
fps=req.get("fps", 24),
size=req.get("size", "1280*720"),
api_key=os.getenv("DASHSCOPE_API_KEY"),
)
final = VideoSynthesis.wait(task)
video_url = final.output.get("video_url")
(prompt, negative_prompt, duration, fps, size, seed, reference_image hash, motion_strength).reference_image can be a URL or local path; the SDK auto-uploads local files.Field required: input.img_url, the reference image is missing or not mapped.wan2.6-t2v and wan2.6-t2v-us add multi-shot narrative support and optional audio input according to the official docs.WxH format (e.g. 1280*720).output/alicloud-ai-video-wan-video/videos/OUTPUT_DIR.See references/api_reference.md for DashScope SDK mapping and async handling notes.
Source list: references/sources.md
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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alicloud-ai-video-wan-video is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in alicloud-ai-video-wan-video — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: alicloud-ai-video-wan-video is focused, and the summary matches what you get after install.
Useful defaults in alicloud-ai-video-wan-video — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
alicloud-ai-video-wan-video is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
alicloud-ai-video-wan-video has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for alicloud-ai-video-wan-video matched our evaluation — installs cleanly and behaves as described in the markdown.
alicloud-ai-video-wan-video reduced setup friction for our internal harness; good balance of opinion and flexibility.
alicloud-ai-video-wan-video fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: alicloud-ai-video-wan-video is focused, and the summary matches what you get after install.
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